-
<<<<<<< HEAD
- The COVID-19 pandemic is caused by the highly infectious virus; SARS-CoV-2.
- CD8+ T cells perform essential functions in the control and elimination of viral infections
- Here, we analyze the immunophenotypes of CD8 T cells that can recognize peptides from the SARS-CoV-2 genome
- We compare COVID-19 patients with healthy donors (HD) never exposed to the virus.
- Furthermore, we compare the phenotypes to the severity of the disease.
- We also compare the phenotype of the SARS-CoV-2 specific CD8 T cells with T cells that recognize common viruses.
11 May 2021
Introduction
Materials and methods
Phenotype data
- Phenotyping of immune cells provides imformation abouts the activation status of the cells
- This is obtained through antibody staining and flow cytometry, which transforms a fluorescent signal into a voltage.
- Cell populations of specific phenotype is gated in FlowJo software, and frequencies of positive cells can afterwards exported as an .xlsx file
Raw data
- The dataset is obtained from S. K. Saini et al., Sci. Immunol. (2021) =======
- Introduction
- Materials - Dataset
- Method
- Results
- Discussion
INTRODUCTION
-The project utilizes data of immuno phenotyping analysis on Covid patient samples (flow cytometry data), which are then plotted to make analysis based on the changes in cell-surface markers expressed when the patient is infected with SARS-Cov2. -The data used was generated at DTU Healthtech - Department of Experimental and translational immunology. -It is then proceeded to wrangling of data obtained from analysis of patient samples on the onset of Covid symptoms. -The wrangled data is then plotted to compare the cell surface markers between different patients to understand the changes of the immune cells during the phase of Covid. -Here we compare the immune profiles of patients who were hospitalized and throse who were not hospitalized with those of healthy individuals
Materials and methods - Data
- The dataset (.csv) used in the project is a rawdata extracted from .fcs file format (obtained from flowcytometry analysis)
- Wrangling of data - Performed to clean data required for plotting >>>>>>> 69f537b56691f5a81559310fc4a09dbaf793ea64
## # A tibble: 82 x 25 ## SampleID Hospital_status `lymphocytes/sin… `lymphocytes/si… `lymphocytes/si… ## <chr> <chr> <chr> <chr> <chr> ## 1 Patient_1 Hospitalized 28.7 20.399999999999… 1.66 ## 2 Patient_2 Hospitalized 12.5 84.1 44.9 ## 3 Patient_3 Hospitalized 27.3 91.2 16.7 ## 4 Patient_4 Hospitalized 5.9 23.3 8.4600000000000… ## 5 Patient_5 Hospitalized 16.8 40 11.2 ## 6 Patient_6 Hospitalized 4.83 49.2 25.9 ## 7 Patient_7 NA NA 76.7 32.1 ## 8 Patient_8 Hospitalized 1.94 45.6 5.41 ## 9 Patient_9 Hospitalized 3.76 34.1 6.15 ## 10 Patient_… Hospitalized 1.69 31.5 12 ## # … with 72 more rows, and 20 more variables: ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD69 <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/PD-1 <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/HLA-DR <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD27 <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD57 <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA-_CCR7+ <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA-_CCR7- <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA+_CCR7- <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA+_CCR7+ <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_CD39+ <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_CD69+ <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_HLA-DR+ <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_PD-1+ <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD38 <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD39 <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD69 <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/PD-1 <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/HLA-DR <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD27 <chr>, ## # lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD57 <chr>
Data wrangling
Tidying the data set
-
<<<<<<< HEAD
- Change NA values
- Add cohort group as a new variable
- Collapse multiple columns into two columns: Gating and Fraction
- Splitting the gating path into two new columns: Last and parent population
Data- Augmenting
## # A tibble: 1,886 x 8 ## SampleID cohort_type Hospital_status Gating T_cell Parent_populati… ## <chr> <chr> <chr> <chr> <chr> <chr> ## 1 Patient_1 Patient Hospitalized lymphocytes/si… CD8 CD8+ ## 2 Patient_1 Patient Hospitalized lymphocytes/si… CD8 SARS_multimer+ ## 3 Patient_1 Patient Hospitalized lymphocytes/si… CD8 SARS_multimer+ ## 4 Patient_1 Patient Hospitalized lymphocytes/si… CD8 SARS_multimer+ ## 5 Patient_1 Patient Hospitalized lymphocytes/si… CD8 SARS_multimer+ ## 6 Patient_1 Patient Hospitalized lymphocytes/si… CD8 SARS_multimer+ ## 7 Patient_1 Patient Hospitalized lymphocytes/si… CD8 SARS_multimer+ ## 8 Patient_1 Patient Hospitalized lymphocytes/si… CD8 SARS_multimer+ ## 9 Patient_1 Patient Hospitalized lymphocytes/si… CD8 SARS_multimer+ ## 10 Patient_1 Patient Hospitalized lymphocytes/si… CD8 SARS_multimer+ ## # … with 1,876 more rows, and 2 more variables: Last_population <chr>, ## # Fraction <dbl>
Creating plots
- Reading covid_data_augment <<<<<<< HEAD
- Filter on Parent and Last population
- Generate plot using ggplot - Boxplot and dotplot
- Multiple plots viewed as facetwrap
- Used geomsigif with Kruskal test
FCS files
Results
Figure A
Figure B
Figure C
Conclusion
Results
Figures - A and B
Figures - C and E
Figures - D and F
Figures - S7B and S8C
